Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery.

• Main Authors: Sean Ekins (Author), Jair Lage de Siqueira-Neto (Author), Laura-Isobel McCall (Author), Malabika Sarker (Author), Maneesh Yadav (Author), Elizabeth L Ponder (Author), E Adam Kallel (Author), Danielle Kellar (Author), Steven Chen (Author), Michelle Arkin (Author), Barry A Bunin (Author), James H McKerrow (Author), Carolyn Talcott (Author)
• Format: Book
• Published: Public Library of Science (PLoS), 2015-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1935-2727; 1935-2735; 10.1371/journal.pntd.0003878
• Keywords: Arctic medicine. Tropical medicine; RC955-962; Public aspects of medicine; RA1-1270
• Source: PLoS Neglected Tropical Diseases, Vol 9, Iss 6, p e0003878 (2015)

BACKGROUND:Chagas disease is a neglected tropical disease (NTD) caused by the eukaryotic parasite Trypanosoma cruzi. The current clinical and preclinical pipeline for T. cruzi is extremely sparse and lacks drug target diversity. METHODOLOGY/PRINCIPAL FINDINGS:In the present study we developed a computational approach that utilized data from several public whole-cell, phenotypic high throughput screens that have been completed for T. cruzi by the Broad Institute, including a single screen of over 300,000 molecules in the search for chemical probes as part of the NIH Molecular Libraries program. We have also compiled and curated relevant biological and chemical compound screening data including (i) compounds and biological activity data from the literature, (ii) high throughput screening datasets, and (iii) predicted metabolites of T. cruzi metabolic pathways. This information was used to help us identify compounds and their potential targets. We have constructed a Pathway Genome Data Base for T. cruzi. In addition, we have developed Bayesian machine learning models that were used to virtually screen libraries of compounds. Ninety-seven compounds were selected for in vitro testing, and 11 of these were found to have EC50 < 10 μM. We progressed five compounds to an in vivo mouse efficacy model of Chagas disease and validated that the machine learning model could identify in vitro active compounds not in the training set, as well as known positive controls. The antimalarial pyronaridine possessed 85.2% efficacy in the acute Chagas mouse model. We have also proposed potential targets (for future verification) for this compound based on structural similarity to known compounds with targets in T. cruzi. CONCLUSIONS/ SIGNIFICANCE:We have demonstrated how combining chemoinformatics and bioinformatics for T. cruzi drug discovery can bring interesting in vivo active molecules to light that may have been overlooked. The approach we have taken is broadly applicable to other NTDs.